基于微博签到数据的出行行为分析

doi:10.3976/j.issn.1002-4026.2017.06.014

Abstract

Abstract:

Using Python crawler, the location checkin data of Sina Weibo in the year of 2012 were crawled through the Sina API port. The data set consisted of 5,028,980 records. These data were divided into 340 cities or regions above prefecture level. Data statistics showed that there was the largest number of check-in in 3 cities: Beijing, Shanghai and Guangzhou, which revealed that Weibo users were more active there. Furthermore, through correlation analysis, it was found that the Weibo users′ attendance flow in these cities was related to the local GDP, indicating that the level of city economic development would affect the users′ travel behavior. In addition, this paper also divided the major cities into clusters according to the users' trip volume, further confirming that the developed cities were more attractive to Weibo users than other economically underdeveloped cities.

Key words: economic level, correlation, Weibo check-in, clustering

CLC Number:

U491

NIE Qi. Travel behavior analysis based on Weibo checkin data[J].SHANDONG SCIENCE, 2017, 30(6): 87-93.

References

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Travel behavior analysis based on Weibo checkin data

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